On the other side, recent advances in this field focus on unsupervised solutions, by reformulating depth prediction as a novel view synthesis problem.
[1]}, [2]} utilized photometric consistency loss as a self-supervision signal for training neural models. With stereo images and a known camera baseline, [1]}, [2]} synthesize the left view from the right image, and the predicted depth maps of the left view. By minimizing the distance between synthesized images and real images, i.e. the spatial consistency, the parameters of the networks can be recovered via this self-supervision in an end-to-end manner. Besides the spatial consistency, [5]} proposed to apply temporal consistency as a self-supervised signal, by synthesizing the image in the target time frame from the source time frame. At the same time, egomotion is recovered along with the depth estimation. This framework only requires monocular images to learn both the depth maps and egomotion. A number of following works [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]} extended this framework and achieved better performance in depth and egomotion estimation. We refer the readers to Section REF , in which a variety of additional constraints haven been discussed.
